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Generative AI for Beginners
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Lesson 1: Introduction to Generative AI and LLMs
1.1.1 Introduction
1.1.2 What is Generative AI?
1.1.3 What Are Language Models?
1.1.4 Using Language Models
1.1.5 Copilot and AI Agents
1.1.6 Understand Microsoft Copilot
1.1.7 Considerations for Prompts
1.1.8 Extending and Developing Copilot-Like Agents
1.1.9 Summary
Lesson 2: Exploring and Comparing Different LLMs
2.1.1 What is Grounding? & Motivation for Grounding
2.1.2 Use-Cases for Grounding / Retrieval-Augmented Generation (RAG)
2.1.3 A Simple Retrieval-Augmented Generation Model
2.1.4 Semantic Search
2.1.5 Vector Indexes and Data Models and Simple Vector Indexing and Retrieval Model
2.1.6 Vector Indexing and Retrieval Techniques and Tools
2.1.7 Vector Indexing and Retrieval Challenges and Solutions
2.1.8 Search and Retrieval - Working with Metadata, Keyword Search and Plugins
2.1.9 Ranking and Set Compilation, Order Matters and Formatting
2.1.10 Tradeoffs: Speed vs Cost vs Quality/Accuracy and Tradeoffs: Preprocessing vs Runtime
2.1.11 Cost, Speed, Quality and Accuracy
2.1.12 Summary
2.2.1 Base Model Selection
2.2.2 Pre-Production Evaluation
2.2.3 Post-Production Monitoring
2.2.4 Conclusion
Lesson 3: Using Generative AI Responsibly
3.1.1 Introduction
3.1.2 Plan a Responsible Generative AI Solution
3.1.3 Identify Potential Harms
3.1.4 Measure Potential Harms
3.1.5 Mitigate Potential Harms
3.1.6 Operate a Responsible Generative AI Solution
3.1.7 Summary
Lesson 4: Understanding Prompt Engineering Fundamentals
4.1.1 Basics
4.1.2 Scenario-Specific Guidance
4.1.3 Start With Clear Instructions
4.1.4 Repeat Instructions at The End, Prime The Output, Add Clear Syntax and Break The Task Down
4.1.5 Use of Affordances , Chain of Thought Prompting and Meta Prompts
4.1.6 Few-Shot Learning, Use Quotes to Generate a Single Sentence and Specifying The Output Structure
4.1.7 Temperature and Top_p Parameters and Best Practices
4.1.8 Space Efficiency
4.2.1 Understand the Basics and Identify Prompt Components
4.2.2 Craft Clear Instructions
4.2.3 Utilize Examples and Pay Attention to Cueing
4.2.4 Test Arrangements and Give the Model an "Out"
4.2.5 Be Mindful of Token Limits and Mitigate Harm with Responsible AI
4.2.6 Experiment and Iterate and Understand the Different APIs
4.2.7 Start with a Clear System Message and Leverage Few-Shot Learning
4.2.8 Use Explicit Constraints and Combine Techniques for Complex Tasks
Lesson 5: Creating Advanced Prompts
5.1.1 What is a System Message? and Safety System Message Examples
5.1.2 Step-by-Step Authoring Best Practices
5.1.3 Summary of Best Practices
5.1.4 Authoring Techniques
5.2.1 What is a System Message?
5.2.2 System Message Examples
Lesson 6: Building Text Generation Applications
6.1.1 Design Prompts
6.1.2 Classify Text
6.1.3 Trigger Ideas
6.1.4 Conduct Conversations
6.1.5 Transform Text
6.1.6 Complete Partial Text and Code Inputs
6.1.7 Generate Factual Responses and Work With Code
Lesson 7: Building Search Apps Vector Databases
7.1.1 Introduction
7.1.2 What is Vector Search?
7.1.3 Prepare Your Search
7.1.4 Understand Embedding
7.1.5 Summary
7.2.1 Approaches for RAG with Azure AI Search
7.2.2 Custom RAG Pattern for Azure AI Search
7.2.3 Searchable Content in Azure AI Search
7.2.4 Content Retrieval in Azure AI Search
Lesson 8: Building Image Generation Applications
8.1.1 Introduction
8.1.2 What is DALL-E?
8.1.3 Explore DALL-E in Azure AI Studio and Use the Azure OpenAI REST API to Consume DALL-E Models
8.1.4 Summary
8.2.1 Generate Images with Azure OpenAI Service
Lesson 9: Building Low Code AI Applications
9.1.1 Introduction
9.1.2 Use GPT in a Power Automate Cloud Flow
9.1.3 Use GPT in a Power Apps Canvas App
9.1.4 Summary
9.2.1 Introduction to AI Builder
9.2.2 Choose an AI Capability
9.2.3 Create Your First Model
9.2.4 Ways to Use Your Models
9.2.5 Summary
Lesson 10: Integrating External Applications with Function Calling
10.1.1 Function Calling Support
10.1.2 Using Function Calling Responsibly
Lesson 11: Designing UX for AI Applications
11.1.1 Introduction
11.1.2 Collaborative UX
11.1.3 AI Notice Pattern
11.1.4 Suggested Prompts Pattern
11.1.5 Feedback Opportunities Pattern
11.1.6 The Emotional Iron Triangle
Lesson 12: Securing Your Generative AI Applications
12.1.1 What is Red Teaming?
12.1.2 Why is RAI Red Teaming an Important Practice?
12.1.3 Before Testing (Part-1)
12.1.3 Before Testing (Part-2)
12.1.4 During Testing
12.1.5 After Each Round of Testing
12.2.1 Introduction
12.2.2 Understand How Sensitivity Labels Apply in Microsoft 365 Copilot Part - 1
12.2.3 Understand How Sensitivity Labels Apply in Microsoft 365 Copilot Part - 2
12.2.4 Use Endpoint DLP to Pevent Generative AI Data Exposure Part - 1
12.2.5 Use Endpoint DLP to Pevent Generative AI Data Exposure Part - 2
12.2.6 Detect Generative AI Usage With Insider Risk Management Part - 1
12.2.7 Detect Generative AI Usage With Insider Risk Management Part - 2
12.2.8 Case study: Implement Adaptive Protection for AI Data Security Part - 1
12.2.9 Case study: Implement Adaptive Protection for AI Data Security Part - 2
12.2.10 Summary
12.3.1 Introduction
12.3.2 Audit Microsoft 365 Copilot Interactions With Microsoft Purview
12.3.3 Investigate and Delete Copilot Interactions with Microsoft Purview eDiscovery Part - 1
12.3.4 Investigate and Delete Copilot Interactions with Microsoft Purview eDiscovery Part - 2
12.3.5 Manage Microsoft 365 Copilot Retention with Microsoft Purview Part - 1
12.3.6 Manage Microsoft 365 Copilot Retention with Microsoft Purview Part - 2
12.3.7 Monitor Microsoft 365 Copilot Interactions with Communication Compliance Part - 1
12.3.8 Monitor Microsoft 365 Copilot Interactions with Communication Compliance Part - 2
12.3.6 Summary
12.4.1 Introduction
12.4.2 Understand AI Security Risks
12.4.3 Data Security Posture Management (DSPM) for AI Overview
12.4.4 Configure DSPM for AI
12.4.5 Review AI Security Reports
12.4.6 Detect Oversharing Risks
12.4.7 Summary
Lesson 13: The Generative AI Application Lifecycle
13.1.1 GenAIOps Prompt Flow Features
13.1.2 GenAIOps Stages
13.1.3 GenAIOps Process Flow
Lesson 14: Retrieval Augmented Generation (RAG) and Vector Databases
14.1.1 Introduction
14.1.2 Understand Retrieval Augmented Generation (RAG) with Azure OpenAI Service
14.1.3 Add Your Own Data Source
14.1.4 Chat With Your Model Using Your Own Data
14.1.5 Summary
14.2.1 Introduction to Perform Vector Search and Retrieval in Azure AI Search
14.2.2 What is Vector Search?
14.2.3 Prepare Your Search
14.2.4 Understand Embedding
14.2.5 Summary
Lesson 15: Open Source Models
15.1.1 How to Evaluate Foundation Models Using Your Own Test Data
15.1.2 How to Fine-Tune Foundation Models Using Your Own Training Data
15.1.3 Deploying Using The Studio
15.1.4 Import Foundation Models
Lesson 16: AI Agents
16.1.1 What is AutoGen ?
16.1.2 Why AutoGen ?
16.1.3 Conversational Programming
16.1.4 Task Termination
16.1.5 Common Agent Workflow Patterns in AutoGen
16.1.6 Deterministic vs Autonomous Workflows
16.1.7 Deterministic Workflows
16.1.8 Autonomous Workflows
16.1.9 Best Practices with AutoGen
Lesson 17: Fine-Tuning LLMs
17.1.1 Introduction
17.1.2 What is AI?
17.1.3 Azure AI Services Part - 1
17.1.4 Azure AI Services Part - 2
17.1.5 Azure AI Foundry Part - 1
17.1.6 Azure AI Foundry Part - 2
17.1.7 Developer Tools and SDKs
17.1.8 Responsible AI
17.1.9 Summary
17.2.1 What is Fine Tuning with Azure OpenAI?
17.2.2 Why do You Want to Fine-Tune a Model?
17.2.3 What have You Tried so Far?
17.2.5 What Data Are You Going to Use For Fine-Tuning?
17.2.6 How will You Measure The Quality of Your Fine-Tuned Model?
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Generative AI for Beginners
